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In STEM education, many 4-year colleges and universities now get most of their students from community colleges. Students who transfer from community colleges, especially those who are underrepresented, often face problems, such as deciding whether or not to transfer, getting academic and non-academic support during the transfer, and finding a job. Also, program advisors at both 2-year and 4-year colleges and universities face problems because they need to know how their students make transfer decisions and how to help them be successful post-transfer. A data-driven and survey-based study will help establish a solid understanding of the underlying elements contributing to these challenges. In this paper, the researchers first conduct a literature review to identify the critical personal and academic factors that influence the transfer decision, particularly for students from traditionally disadvantaged groups. Secondly, an exploratory analysis of these factors was performed by inviting a small group of computing major students from both community colleges and universities to participate in a survey that includes a wide range of questions, from demographics and pre-transfer decisions to post-transfer performance. The preliminary findings indicated that financial challenges, university reputation, university location, job prospects, and family expectations are the primary factors influencing student transfer decisions. The findings of the study can be beneficial to underrepresented transfer students, their advisors, and other stakeholders in higher education.
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Data science transfer pathways from associate's to bachelor's programs
A substantial fraction of students who complete their college education at a public university in the United States begin their journey at one of the 935 public 2-year colleges. While the number of 4-year colleges offering bachelor’s degrees in data science continues to increase, data science instruction at many 2-year colleges lags behind. A major impediment is the relative paucity of introductory data science courses that serve multiple student audiences and can easily transfer. In addition, the lack of predefined transfer pathways (or articulation agreements) for data science creates a growing disconnect that leaves students who want to study data science at a disadvantage. We describe opportunities and barriers to data science transfer pathways. Five points of curricular friction merit attention: 1) a first course in data science, 2) a second course in data science, 3) a course in scientific computing, data science workflow, and/or reproducible computing, 4) lab sciences, and 5) navigating communication, ethics, and application domain requirements in the context of general education and liberal arts course mappings. We catalog existing transfer pathways, efforts to align curricula across institutions, obstacles to overcome with minimally disruptive solutions, and approaches to foster these pathways. Improvements in these areas are critically important to ensure that a broad and diverse set of students are able to engage and succeed in undergraduate data science programs.
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- PAR ID:
- 10466660
- Editor(s):
- Meng, X-L
- Publisher / Repository:
- Harvard Data Science Review
- Date Published:
- Journal Name:
- Harvard Data Science Review
- Volume:
- 5
- Issue:
- 1
- ISSN:
- 2644-2353
- Subject(s) / Keyword(s):
- articulation, associate’s programs, bachelor’s programs, two-year colleges, community colleges
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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